1
|
Davies-Jenkins CW, Zöllner HJ, Simicic D, Hui SCN, Song Y, Hupfeld KE, Prisciandaro JJ, Edden RAE, Oeltzschner G. GABA-edited MEGA-PRESS at 3 T: Does a measured macromolecule background improve linear combination modeling? Magn Reson Med 2024. [PMID: 38818623 DOI: 10.1002/mrm.30158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 04/30/2024] [Accepted: 05/01/2024] [Indexed: 06/01/2024]
Abstract
PURPOSE The J-difference edited γ-aminobutyric acid (GABA) signal is contaminated by other co-edited signals-the largest of which originates from co-edited macromolecules (MMs)-and is consequently often reported as "GABA+." MM signals are broader and less well-characterized than the metabolites, and are commonly approximated using a Gaussian model parameterization. Experimentally measured MM signals are a consensus-recommended alternative to parameterized modeling; however, they are relatively under-studied in the context of edited MRS. METHODS To address this limitation in the literature, we have acquired GABA-edited MEGA-PRESS data with pre-inversion to null metabolite signals in 13 healthy controls. An experimental MM basis function was derived from the mean across subjects. We further derived a new parameterization of the MM signals from the experimental data, using multiple Gaussians to accurately represent their observed asymmetry. The previous single-Gaussian parameterization, mean experimental MM spectrum and new multi-Gaussian parameterization were compared in a three-way analysis of a public MEGA-PRESS dataset of 61 healthy participants. RESULTS Both the experimental MMs and the multi-Gaussian parameterization exhibited reduced fit residuals compared to the single-Gaussian approach (p = 0.034 and p = 0.031, respectively), suggesting they better represent the underlying data than the single-Gaussian parameterization. Furthermore, both experimentally derived models estimated larger MM fractional contribution to the GABA+ signal for the experimental MMs (58%) and multi-Gaussian parameterization (58%), compared to the single-Gaussian approach (50%). CONCLUSIONS Our results indicate that single-Gaussian parameterization of edited MM signals is insufficient and that both experimentally derived GABA+ spectra and their parameterized replicas improve the modeling of GABA+ spectra.
Collapse
Affiliation(s)
- Christopher W Davies-Jenkins
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Helge J Zöllner
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Dunja Simicic
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Steve C N Hui
- Developing Brain Institute, Children's National Hospital, Washington, District of Columbia, USA
- Department of Radiology, The George Washington School of Medicine and Health Sciences, Washington, District of Columbia, USA
- Department of Pediatrics, The George Washington School of Medicine and Health Sciences, Washington, District of Columbia, USA
| | - Yulu Song
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Kathleen E Hupfeld
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - James J Prisciandaro
- Department of Psychiatry and Behavioral Sciences, Addiction Sciences Division, Center for Biomedical Imaging, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Richard A E Edden
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Georg Oeltzschner
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| |
Collapse
|
2
|
Song Y, Guo SH, Davies-Jenkins CW, Guarda A, Edden RAE, Smith KR. From Molecules to Behavior: Linking Brain Biochemistry in the Dorsal Anterior Cingulate Cortex to 'Anxiety to Eat' Responses in Anorexia Nervosa. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.29.596476. [PMID: 38854088 PMCID: PMC11160692 DOI: 10.1101/2024.05.29.596476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
Background Anorexia nervosa (AN) is a mental and behavioral health condition characterized by an intense fear of weight or fat gain, severe restriction of food intake resulting in low body weight, and distorted self-perception of body shape or weight. While substantial research has focused on general anxiety in AN, less is known about eating-related anxiety and its underlying neural mechanisms. Therefore, we sought to characterize 'anxiety to eat' in AN and examine the neurometabolic profile within the dorsal anterior cingulate cortex (dACC), a brain region putatively involved in magnifying the threat response. Methods Women seeking inpatient treatment for AN and women of healthy weight without a lifetime history of an eating disorder (healthy controls; HC) completed a computer-based behavioral task assessing 'anxiety to eat' in response to images of higher (HED) and lower (LED) energy density foods. Participants also underwent magnetic resonance spectroscopy of the dACC in a 3T scanner. Results The AN group reported greater anxiety to eat both HED and LED foods relative to the HC group. Both groups reported greater anxiety to eat HED foods relative to LED foods. The neurometabolite myo-inositol (mI) was lower in the dACC in AN relative to HC, and mI levels negatively predicted anxiety to eat HED but not LED foods in the AN group only. mI levels in the dACC were independent of body weight, body mass, and general anxiety. Conclusions These findings provide critical new insight into the clinically challenging feature and underlying neural mechanisms of eating-related anxiety, and indicate mI levels in the dACC could serve as a novel biomarker of illness severity that is independent of body weight to identify individuals vulnerable to disordered eating or eating pathology as well as a potential therapeutic target.
Collapse
|
3
|
Zöllner HJ, Davies-Jenkins C, Simicic D, Tal A, Sulam J, Oeltzschner G. Simultaneous multi-transient linear-combination modeling of MRS data improves uncertainty estimation. Magn Reson Med 2024. [PMID: 38649977 DOI: 10.1002/mrm.30110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 03/05/2024] [Accepted: 03/24/2024] [Indexed: 04/25/2024]
Abstract
PURPOSE The interest in applying and modeling dynamic MRS has recently grown. Two-dimensional modeling yields advantages for the precision of metabolite estimation in interrelated MRS data. However, it is unknown whether including all transients simultaneously in a 2D model without averaging (presuming a stable signal) performs similarly to one-dimensional (1D) modeling of the averaged spectrum. Therefore, we systematically investigated the accuracy, precision, and uncertainty estimation of both described model approaches. METHODS Monte Carlo simulations of synthetic MRS data were used to compare the accuracy and uncertainty estimation of simultaneous 2D multitransient linear-combination modeling (LCM) with 1D-LCM of the average. A total of 2,500 data sets per condition with different noise representations of a 64-transient MRS experiment at six signal-to-noise levels for two separate spin systems (scyllo-inositol and gamma-aminobutyric acid) were analyzed. Additional data sets with different levels of noise correlation were also analyzed. Modeling accuracy was assessed by determining the relative bias of the estimated amplitudes against the ground truth, and modeling precision was determined by SDs and Cramér-Rao lower bounds (CRLBs). RESULTS Amplitude estimates for 1D- and 2D-LCM agreed well and showed a similar level of bias compared with the ground truth. Estimated CRLBs agreed well between both models and with ground-truth CRLBs. For correlated noise, the estimated CRLBs increased with the correlation strength for the 1D-LCM but remained stable for the 2D-LCM. CONCLUSION Our results indicate that the model performance of 2D multitransient LCM is similar to averaged 1D-LCM. This validation on a simplified scenario serves as a necessary basis for further applications of 2D modeling.
Collapse
Affiliation(s)
- Helge Jörn Zöllner
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Christopher Davies-Jenkins
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Dunja Simicic
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Assaf Tal
- Department of Chemical and Biological Physics, Weizmann Institute of Science, Rehovot, Israel
| | - Jeremias Sulam
- Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, Maryland, USA
- Mathematical Institute for Data Science, The Johns Hopkins University, Baltimore, Maryland, USA
| | - Georg Oeltzschner
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| |
Collapse
|
4
|
Hui SC, Murali-Manohar S, Zöllner HJ, Hupfeld KE, Davies-Jenkins CW, Gudmundson AT, Song Y, Yedavalli V, Wisnowski JL, Gagoski B, Oeltzschner G, Edden RA. Integrated Short-TE and Hadamard-edited Multi-Sequence (ISTHMUS) for Advanced MRS. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.15.580516. [PMID: 38659947 PMCID: PMC11042202 DOI: 10.1101/2024.02.15.580516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
Background To examine data quality and reproducibility using ISTHMUS, which has been implemented as the standardized MR spectroscopy sequence for the multi-site Healthy Brain and Child Development (HBCD) study. Methods ISTHMUS is the consecutive acquisition of short-TE PRESS (32 transients) and long-TE HERCULES (224 transients) data with dual-TE water reference scans. Voxels were positioned in the centrum semiovale, dorsal anterior cingulate cortex, posterior cingulate cortex and bilateral thalamus regions. After acquisition, ISTHMUS data were separated into the PRESS and HERCULES portions for analysis and modeled separately using Osprey. In vivo experiments were performed in 10 healthy volunteers (6 female; 29.5±6.6 years). Each volunteer underwent two scans on the same day. Differences in metabolite measurements were examined. T2 correction based on the dual-TE water integrals were compared with: 1) T2 correction based the default white matter and gray matter T2 reference values in Osprey; 2) shorter WM and GM T2 values from recent literature; and 3) reduced CSF fractions. Results No significant difference in linewidth was observed between PRESS and HERCULES. Bilateral thalamus spectra had produced significantly higher (p<0.001) linewidth compared to the other three regions. Linewidth measurements were similar between scans, with scan-to-scan differences under 1 Hz for most subjects. Paired t-tests indicated a significant difference only in PRESS NAAG between the two thalamus scans (p=0.002). T2 correction based on shorter T2 values showed better agreement to the dual-TE water integral ratio. Conclusions ISTHMUS facilitated and standardized acquisition and post-processing and reduced operator workload to eliminate potential human error.
Collapse
Affiliation(s)
- Steve C.N. Hui
- Developing Brain Institute, Children’s National Hospital, Washington, D.C. USA
- Departments of Radiology, The George Washington University School of Medicine and Health Sciences, Washington, D.C. USA
- Departments of Pediatrics, The George Washington University School of Medicine and Health Sciences, Washington, D.C. USA
| | - Saipavitra Murali-Manohar
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Helge J. Zöllner
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Kathleen E. Hupfeld
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Christopher W. Davies-Jenkins
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Aaron T. Gudmundson
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Yulu Song
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Vivek Yedavalli
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Jessica L Wisnowski
- Department of Radiology, Children’s Hospital Los Angeles, Los Angeles, CA, USA
- Department of Pediatrics, Children’s Hospital Los Angeles, Los Angeles, CA, USA
| | - Borjan Gagoski
- Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children’s Hospital, Boston, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Georg Oeltzschner
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Richard A.E. Edden
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| |
Collapse
|
5
|
Dounavi ME, McKiernan E, Langsen M, Gregory S, Muniz-Terrera G, Prats-Sedano MA, Mada MO, Williams GB, Lawlor B, Naci L, Mackay C, Koychev I, Malhotra P, Ritchie K, Ritchie CW, Su L, Waldman AD, O’ Brien JT. Investigating the brain's neurochemical profile at midlife in relation to dementia risk factors. Brain Commun 2024; 6:fcae138. [PMID: 38779354 PMCID: PMC11109818 DOI: 10.1093/braincomms/fcae138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 01/18/2024] [Accepted: 04/15/2024] [Indexed: 05/25/2024] Open
Abstract
Changes in the brain's physiology in Alzheimer's disease are thought to occur early in the disease's trajectory. In this study our aim was to investigate the brain's neurochemical profile in a midlife cohort in relation to risk factors for future dementia using single voxel proton magnetic resonance spectroscopy. Participants in the multi-site PREVENT-Dementia study (age range 40-59 year old) underwent 3T magnetic resonance spectroscopy with the spectroscopy voxel placed in the posterior cingulate/precuneus region. Using LCModel, we quantified the absolute concentrations of myo-inositol, total N-acetylaspartate, total creatine, choline, glutathione and glutamate-glutamine for 406 participants (mean age 51.1; 65.3% female). Underlying partial volume effects were accounted for by applying a correction for the presence of cerebrospinal fluid in the magnetic resonance spectroscopy voxel. We investigated how metabolite concentrations related to apolipoprotein ɛ4 genotype, dementia family history, a risk score (Cardiovascular Risk Factors, Aging and Incidence of Dementia -CAIDE) for future dementia including non-modifiable and potentially-modifiable factors and dietary patterns (adherence to Mediterranean diet). Dementia family history was associated with decreased total N-acetylaspartate and no differences were found between apolipoprotein ɛ4 carriers and non-carriers. A higher Cardiovascular Risk Factors, Aging, and Incidence of Dementia score related to higher myo-inositol, choline, total creatine and glutamate-glutamine, an effect which was mainly driven by older age and a higher body mass index. Greater adherence to the Mediterranean diet was associated with lower choline, myo-inositol and total creatine; these effects did not survive correction for multiple comparisons. The observed associations suggest that at midlife the brain demonstrates subtle neurochemical changes in relation to both inherited and potentially modifiable risk factors for future dementia.
Collapse
Affiliation(s)
- Maria-Eleni Dounavi
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, CB2 0SP, UK
| | - Elizabeth McKiernan
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, CB2 0SP, UK
| | - Michael Langsen
- Center for Clinical Brain Sciences, University of Edinburgh, Edinburgh, EH16 4SB, UK
| | - Sarah Gregory
- Centre for Dementia Prevention, University of Edinburgh, Edinburgh, EH16 4UX, UK
| | - Graciela Muniz-Terrera
- Centre for Dementia Prevention, University of Edinburgh, Edinburgh, EH16 4UX, UK
- Heritage College of Osteopathic Medicine, Ohio University, Athens, OH 45701, USA
| | | | - Marius Ovidiu Mada
- Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, CB2 7EF, UK
| | - Guy B Williams
- Department of Clinical Neurosciences and Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Brian Lawlor
- Institute of Neuroscience, Trinity College Dublin, University of Dublin, Dublin, D02 PX31, Ireland
| | - Lorina Naci
- Institute of Neuroscience, Trinity College Dublin, University of Dublin, Dublin, D02 PX31, Ireland
| | - Clare Mackay
- Department of Psychiatry, Oxford University, Oxford, OX3 7JX, UK
| | - Ivan Koychev
- Department of Psychiatry, Oxford University, Oxford, OX3 7JX, UK
| | - Paresh Malhotra
- Department of Brain Sciences, Imperial College Healthcare NHS Trust, London, W12 0NN, UK
| | - Karen Ritchie
- INM, Univ Montpellier, INSERM, Montpellier, 34090, France
| | - Craig W Ritchie
- Centre for Dementia Prevention, University of Edinburgh, Edinburgh, EH16 4UX, UK
| | - Li Su
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, CB2 0SP, UK
- Sheffield Institute of Translational Neuroscience, University of Sheffield, Sheffield, S10 2HQ, UK
| | - Adam D Waldman
- Center for Clinical Brain Sciences, University of Edinburgh, Edinburgh, EH16 4SB, UK
- Department of Brain Sciences, Imperial College Healthcare NHS Trust, London, W12 0NN, UK
| | - John T O’ Brien
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, CB2 0SP, UK
| |
Collapse
|
6
|
Murali-Manohar S, Gudmundson AT, Hupfeld KE, Zöllner HJ, Hui SCN, Song Y, Simicic D, Davies-Jenkins CW, Gong T, Wang G, Oeltzschner G, Edden RAE. Metabolite T 1 relaxation times decrease across the adult lifespan. NMR IN BIOMEDICINE 2024:e5152. [PMID: 38565525 DOI: 10.1002/nbm.5152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 01/08/2024] [Accepted: 03/02/2024] [Indexed: 04/04/2024]
Abstract
Relaxation correction is an integral step in quantifying brain metabolite concentrations measured by in vivo magnetic resonance spectroscopy (MRS). While most quantification routines assume constant T1 relaxation across age, it is possible that aging alters T1 relaxation rates, as is seen for T2 relaxation. Here, we investigate the age dependence of metabolite T1 relaxation times at 3 T in both gray- and white-matter-rich voxels using publicly available metabolite and metabolite-nulled (single inversion recovery TI = 600 ms) spectra acquired at 3 T using Point RESolved Spectroscopy (PRESS) localization. Data were acquired from voxels in the posterior cingulate cortex (PCC) and centrum semiovale (CSO) in 102 healthy volunteers across 5 decades of life (aged 20-69 years). All spectra were analyzed in Osprey v.2.4.0. To estimate T1 relaxation times for total N-acetyl aspartate at 2.0 ppm (tNAA2.0) and total creatine at 3.0 ppm (tCr3.0), the ratio of modeled metabolite residual amplitudes in the metabolite-nulled spectrum to the full metabolite signal was calculated using the single-inversion-recovery signal equation. Correlations between T1 and subject age were evaluated. Spearman correlations revealed that estimated T1 relaxation times of tNAA2.0 (rs = -0.27; p < 0.006) and tCr3.0 (rs = -0.40; p < 0.001) decreased significantly with age in white-matter-rich CSO, and less steeply for tNAA2.0 (rs = -0.228; p = 0.005) and (not significantly for) tCr3.0 (rs = -0.13; p = 0.196) in graymatter-rich PCC. The analysis harnessed a large publicly available cross-sectional dataset to test an important hypothesis, that metabolite T1 relaxation times change with age. This preliminary study stresses the importance of further work to measure age-normed metabolite T1 relaxation times for accurate quantification of metabolite levels in studies of aging.
Collapse
Affiliation(s)
- Saipavitra Murali-Manohar
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Aaron T Gudmundson
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Kathleen E Hupfeld
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Helge J Zöllner
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Steve C N Hui
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Yulu Song
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Dunja Simicic
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Christopher W Davies-Jenkins
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Tao Gong
- Departments of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
- Departments of Radiology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, China
| | - Guangbin Wang
- Departments of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
- Departments of Radiology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, China
| | - Georg Oeltzschner
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Richard A E Edden
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| |
Collapse
|
7
|
Saleh MG, Prescot A, Chang L, Cloak C, Cunningham E, Subramaniam P, Renshaw PF, Yurgelun-Todd D, Zöllner HJ, Roberts TPL, Edden RAE, Ernst T. Glutamate measurements using edited MRS. Magn Reson Med 2024; 91:1314-1322. [PMID: 38044723 PMCID: PMC10865745 DOI: 10.1002/mrm.29929] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 10/02/2023] [Accepted: 10/31/2023] [Indexed: 12/05/2023]
Abstract
PURPOSE To demonstrate J-difference coediting of glutamate using Hadamard encoding and reconstruction of Mescher-Garwood-edited spectroscopy (HERMES). METHODS Density-matrix simulations of HERMES (TE 80 ms) and 1D J-resolved (TE 31-229 ms) of glutamate (Glu), glutamine (Gln), γ-aminobutyric acid (GABA), and glutathione (GSH) were performed. HERMES comprised four sub-experiments with editing pulses applied as follows: (A) 1.9/4.56 ppm simultaneously (ONGABA /ONGSH ); (B) 1.9 ppm only (ONGABA /OFFGSH ); (C) 4.56 ppm only (OFFGABA /ONGSH ); and (D) 7.5 ppm (OFFGABA /OFFGSH ). Phantom HERMES and 1D J-resolved experiments of Glu were performed. Finally, in vivo HERMES (20-ms editing pulses) and 1D J-resolved (TE 31-229 ms) experiments were performed on 137 participants using 3 T MRI scanners. LCModel was used for quantification. RESULTS HERMES simulation and phantom experiments show a Glu-edited signal at 2.34 ppm in the Hadamard sum combination A+B+C+D with no overlapping Gln signal. The J-resolved simulations and phantom experiments show substantial TE modulation of the Glu and Gln signals across the TEs, whose average yields a well-resolved Glu signal closely matching the Glu-edited signal from the HERMES sum spectrum. In vivo quantification of Glu show that the two methods are highly correlated (p < 0.001) with a bias of ∼10%, along with similar between-subject coefficients of variation (HERMES/TE-averaged: ∼7.3%/∼6.9%). Other Hadamard combinations produce the expected GABA-edited (A+B-C-D) or GSH-edited (A-B+C-D) signal. CONCLUSION HERMES simulation and phantom experiments show the separation of Glu from Gln. In vivo HERMES experiments yield Glu (without Gln), GABA, and GSH in a single MRS scan.
Collapse
Affiliation(s)
- Muhammad G Saleh
- Lurie Family Foundations MEG Imaging Center, Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Andrew Prescot
- Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah, USA
| | - Linda Chang
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Christine Cloak
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Eric Cunningham
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Punitha Subramaniam
- Department of Psychiatry, University of Utah, Salt Lake City, Utah, USA
- Diagnostic Neuroimaging Laboratory, University of Utah, Salt Lake City, Utah, USA
| | - Perry F Renshaw
- Department of Psychiatry, University of Utah, Salt Lake City, Utah, USA
- Diagnostic Neuroimaging Laboratory, University of Utah, Salt Lake City, Utah, USA
| | - Deborah Yurgelun-Todd
- Department of Psychiatry, University of Utah, Salt Lake City, Utah, USA
- Diagnostic Neuroimaging Laboratory, University of Utah, Salt Lake City, Utah, USA
| | - Helge J Zöllner
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Timothy P L Roberts
- Lurie Family Foundations MEG Imaging Center, Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Richard A E Edden
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Thomas Ernst
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Maryland, USA
| |
Collapse
|
8
|
Simicic D, Zöllner HJ, Davies-Jenkins CW, Hupfeld KE, Edden RAE, Oeltzschner G. Model-based frequency-and-phase correction of 1H MRS data with 2D linear-combination modeling. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.26.586804. [PMID: 38585798 PMCID: PMC10996641 DOI: 10.1101/2024.03.26.586804] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Purpose Retrospective frequency-and-phase correction (FPC) methods attempt to remove frequency-and-phase variations between transients to improve the quality of the averaged MR spectrum. However, traditional FPC methods like spectral registration struggle at low SNR. Here, we propose a method that directly integrates FPC into a two-dimensional linear-combination model (2D-LCM) of individual transients ('model-based FPC'). We investigated how model-based FPC performs compared to the traditional approach, i.e., spectral registration followed by 1D-LCM in estimating frequency-and-phase drifts and, consequentially, metabolite level estimates. Methods We created synthetic in-vivo-like 64-transient short-TE sLASER datasets with 100 noise realizations at 5 SNR levels and added randomly sampled frequency and phase variations. We then used this synthetic dataset to compare the performance of 2D-LCM with the traditional approach (spectral registration, averaging, then 1D-LCM). Outcome measures were the frequency/phase/amplitude errors, the standard deviation of those ground-truth errors, and amplitude Cramér Rao Lower Bounds (CRLBs). We further tested the proposed method on publicly available in-vivo short-TE PRESS data. Results 2D-LCM estimates (and accounts for) frequency-and-phase variations directly from uncorrected data with equivalent or better fidelity than the conventional approach. Furthermore, 2D-LCM metabolite amplitude estimates were at least as accurate, precise, and certain as the conventionally derived estimates. 2D-LCM estimation of frequency and phase correction and amplitudes performed substantially better at low-to-very-low SNR. Conclusion Model-based FPC with 2D linear-combination modeling is feasible and has great potential to improve metabolite level estimation for conventional and dynamic MRS data, especially for low-SNR conditions, e.g., long TEs or strong diffusion weighting.
Collapse
Affiliation(s)
- Dunja Simicic
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Helge J. Zöllner
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Christopher W. Davies-Jenkins
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Kathleen E. Hupfeld
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Richard A. E. Edden
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Georg Oeltzschner
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| |
Collapse
|
9
|
Song Y, Hupfeld KE, Davies-Jenkins CW, Zöllner HJ, Murali-Manohar S, Mumuni AN, Crocetti D, Yedavalli V, Oeltzschner G, Alessi N, Batschelett MA, Puts NA, Mostofsky SH, Edden RA. Brain glutathione and GABA+ levels in autistic children. Autism Res 2024; 17:512-528. [PMID: 38279628 PMCID: PMC10963146 DOI: 10.1002/aur.3097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 12/28/2023] [Indexed: 01/28/2024]
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by social communication challenges and repetitive behaviors. Altered neurometabolite levels, including glutathione (GSH) and gamma-aminobutyric acid (GABA), have been proposed as potential contributors to the biology underlying ASD. This study investigated whether cerebral GSH or GABA levels differ between a cohort of children aged 8-12 years with ASD (n = 52) and typically developing children (TDC, n = 49). A comprehensive analysis of GSH and GABA levels in multiple brain regions, including the primary motor cortex (SM1), thalamus (Thal), medial prefrontal cortex (mPFC), and supplementary motor area (SMA), was conducted using single-voxel HERMES MR spectroscopy at 3T. The results revealed no significant differences in cerebral GSH or GABA levels between the ASD and TDC groups across all examined regions. These findings suggest that the concentrations of GSH (an important antioxidant and neuromodulator) and GABA (a major inhibitory neurotransmitter) do not exhibit marked alterations in children with ASD compared to TDC. A statistically significant positive correlation was observed between GABA levels in the SM1 and Thal regions with ADHD inattention scores. No significant correlation was found between metabolite levels and hyper/impulsive scores of ADHD, measures of core ASD symptoms (ADOS-2, SRS-P) or adaptive behavior (ABAS-2). While both GSH and GABA have been implicated in various neurological disorders, the current study provides valuable insights into the specific context of ASD and highlights the need for further research to explore other neurochemical alterations that may contribute to the pathophysiology of this complex disorder.
Collapse
Affiliation(s)
- Yulu Song
- The Russel H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Kathleen E. Hupfeld
- The Russel H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Christopher W. Davies-Jenkins
- The Russel H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Helge J. Zöllner
- The Russel H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Saipavitra Murali-Manohar
- The Russel H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| | | | - Deana Crocetti
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Vivek Yedavalli
- The Russel H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Georg Oeltzschner
- The Russel H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Natalie Alessi
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Mitchell A. Batschelett
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Nicolaas A.J. Puts
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology, and Neuroscience, King’s College London, London, United Kingdom
- MRC Center for Neurodevelopmental Disorders, King’s College London, London, United Kingdom
| | - Stewart H. Mostofsky
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, United States
- Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Department of Psychiatry and Behavioral Sciences, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Richard A.E. Edden
- The Russel H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| |
Collapse
|
10
|
Hui SC, Zöllner HJ, Gong T, Hupfeld KE, Gudmundson AT, Murali-Manohar S, Davies-Jenkins CW, Song Y, Chen Y, Oeltzschner G, Wang G, Edden RAE. sLASER and PRESS perform similarly at revealing metabolite-age correlations at 3 T. Magn Reson Med 2024; 91:431-442. [PMID: 37876339 PMCID: PMC10942734 DOI: 10.1002/mrm.29895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 10/03/2023] [Accepted: 10/04/2023] [Indexed: 10/26/2023]
Abstract
PURPOSE To compare the respective ability of PRESS and sLASER to reveal biological relationships, using age as a validation covariate at 3 T. METHODS MRS data were acquired from 102 healthy volunteers using PRESS and sLASER in centrum semiovale and posterior cingulate cortex (PCC). Acquisition parameters included TR/TE = 2000/30 ms, 96 transients, and 2048 datapoints sampled at 2 kHz. Spectra were analyzed using Osprey. SNR, FWHM linewidth of total creatine, and metabolite concentrations were extracted. A linear model was used to compare SNR and linewidth. Paired t-tests were used to assess differences in metabolite measurements between PRESS and sLASER. Correlations were used to evaluate the relationship between PRESS and sLASER metabolite estimates, as well as the strength of each metabolite-age relationship. Coefficients of variation were calculated to assess inter-subject variability in each metabolite measurement. RESULTS SNR and linewidth were significantly higher (p < 0.01) for sLASER than PRESS in PCC. Paired t-tests showed significant differences between PRESS and sLASER in most metabolite measurements. PRESS-sLASER measurements were significantly correlated (p < 0.05) for most metabolites. Metabolite-age relationships were consistently identified using both methods. Similar coefficients of variation were observed for most metabolites. CONCLUSION The study results suggest strong agreement between PRESS and sLASER in identifying relationships between brain metabolites and age in centrum semiovale and PCC data acquired at 3 T. sLASER is technically desirable due to the reduced chemical shift displacement artifact; however, PRESS performed similarly in homogeneous brain regions at clinical field strength.
Collapse
Affiliation(s)
- Steve C.N. Hui
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
- Developing Brain Institute, Children’s National Hospital, Washington, DC, USA
| | - Helge J. Zöllner
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Tao Gong
- Department of Radiology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Kathleen E. Hupfeld
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Aaron T. Gudmundson
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Saipavitra Murali-Manohar
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Christopher W. Davies-Jenkins
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Yulu Song
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Yufan Chen
- Department of Radiology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Georg Oeltzschner
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Guangbin Wang
- Department of Radiology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Richard A. E. Edden
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| |
Collapse
|
11
|
Zöllner HJ, Davies-Jenkins C, Simicic D, Tal A, Sulam J, Oeltzschner G. Simultaneous multi-transient linear-combination modeling of MRS data improves uncertainty estimation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.11.01.565164. [PMID: 38260650 PMCID: PMC10802456 DOI: 10.1101/2023.11.01.565164] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Purpose The interest in applying and modeling dynamic MRS has recently grown. 2D modeling yields advantages for the precision of metabolite estimation in interrelated MRS data. However, it is unknown whether including all transients simultaneously in a 2D model without averaging (presuming a stable signal) performs similarly to 1D modeling of the averaged spectrum. Therefore, we systematically investigated the accuracy, precision, and uncertainty estimation of both described model approaches. Methods Monte Carlo simulations of synthetic MRS data were used to compare the accuracy and uncertainty estimation of simultaneous 2D multi-transient LCM with 1D-LCM of the average. 2,500 datasets per condition with different noise representations of a 64-transient MRS experiment at 6 signal-to-noise levels for two separate spin systems (scyllo-inositol and GABA) were analyzed. Additional datasets with different levels of noise correlation were also analyzed. Modeling accuracy was assessed by determining the relative bias of the estimated amplitudes against the ground truth, and modeling precision was determined by standard deviations and Cramér-Rao Lower Bounds (CRLB). Results Amplitude estimates for 1D- and 2D-LCM agreed well and showed similar level of bias compared to the ground truth. Estimated CRLBs agreed well between both models and with ground truth CRLBs. For correlated noise the estimated CRLBs increased with the correlation strength for the 1D-LCM but remained stable for the 2D-LCM. Conclusion Our results indicate that the model performance of 2D multi-transient LCM is similar to averaged 1D-LCM. This validation on a simplified scenario serves as necessary basis for further applications of 2D modeling.
Collapse
Affiliation(s)
- Helge J. Zöllner
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Christopher Davies-Jenkins
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Dunja Simicic
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Assaf Tal
- Department of Chemical and Biological Physics, Weizmann Institute of Science, Rehovot, Israel
| | - Jeremias Sulam
- Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, MD, United States
- Mathematical Institute for Data Science, The Johns Hopkins University, Baltimore, MD, United States
| | - Georg Oeltzschner
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| |
Collapse
|
12
|
Chen X, Li J, Chen D, Zhou Y, Tu Z, Lin M, Kang T, Lin J, Gong T, Zhu L, Zhou J, Lin OY, Guo J, Dong J, Guo D, Qu X. CloudBrain-MRS: An intelligent cloud computing platform for in vivo magnetic resonance spectroscopy preprocessing, quantification, and analysis. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2024; 358:107601. [PMID: 38039654 DOI: 10.1016/j.jmr.2023.107601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 11/26/2023] [Accepted: 11/27/2023] [Indexed: 12/03/2023]
Abstract
Magnetic resonance spectroscopy (MRS) is an important clinical imaging method for diagnosis of diseases. MRS spectrum is used to observe the signal intensity of metabolites or further infer their concentrations. Although the magnetic resonance vendors commonly provide basic functions of spectrum plots and metabolite quantification, the spread of clinical research of MRS is still limited due to the lack of easy-to-use processing software or platform. To address this issue, we have developed CloudBrain-MRS, a cloud-based online platform that provides powerful hardware and advanced algorithms. The platform can be accessed simply through a web browser, without the need of any program installation on the user side. CloudBrain-MRS also integrates the classic LCModel and advanced artificial intelligence algorithms and supports batch preprocessing, quantification, and analysis of MRS data from different vendors. Additionally, the platform offers useful functions: (1) Automatically statistical analysis to find biomarkers for diseases; (2) Consistency verification between the classic and artificial intelligence quantification algorithms; (3) Colorful three-dimensional visualization for easy observation of individual metabolite spectrum. Last, data of both healthy subjects and patients with mild cognitive impairment are used to demonstrate the functions of the platform. To the best of our knowledge, this is the first cloud computing platform for in vivo MRS with artificial intelligence processing. We have shared our cloud platform at MRSHub, providing at least two years of free access and service. If you are interested, please visit https://mrshub.org/software_all/#CloudBrain-MRS or https://csrc.xmu.edu.cn/CloudBrain.html.
Collapse
Affiliation(s)
- Xiaodie Chen
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China
| | - Jiayu Li
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China
| | - Dicheng Chen
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China
| | - Yirong Zhou
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China
| | - Zhangren Tu
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China
| | - Meijin Lin
- Department of Applied Marine Physics & Engineering, Xiamen University, Xiamen, China
| | - Taishan Kang
- Department of Radiology, Zhongshan Hospital Affiliated to Xiamen University, Xiamen, China
| | - Jianzhong Lin
- Department of Radiology, Zhongshan Hospital Affiliated to Xiamen University, Xiamen, China
| | - Tao Gong
- Departments of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Liuhong Zhu
- Department of Radiology, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, China
| | - Jianjun Zhou
- Department of Radiology, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, China
| | - Ou-Yang Lin
- Department of Medical Imaging of Southeast Hospital, Medical College of Xiamen University, Xiamen, China
| | - Jiefeng Guo
- Department of Microelectronics and Integrated Circuit, Xiamen University, Xiamen, China
| | - Jiyang Dong
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China
| | - Di Guo
- School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China
| | - Xiaobo Qu
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China.
| |
Collapse
|
13
|
Rustamzadeh A, Sadigh N, Shabani R, Ahadi R, Vahabi Z, Shabani A, Mohebi N, Khamseh F, Behruzi M, Moradi F. Neurochemical Ameliorating of the Hippocampus in Dyslipidemic Alzheimer Patients Following Silymarin; a Double-Blind Placebo-Controlled Randomized Clinical Trial. Med J Islam Repub Iran 2023; 37:123. [PMID: 38318412 PMCID: PMC10843210 DOI: 10.47176/mjiri.37.123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Indexed: 02/07/2024] Open
Abstract
Background Amyloid-beta (Aβ) production is a normal physiological process, and an imbalance in Aβ production/excretion rate is the basis of the plaque load increase in AD. LRP1 is involved in both central clearance of Aβ from the CNS and transport of Aβ toward peripheral organs. In this study, the effect of silymarin combination compared to rosuvastatin and placebo on neuro-metabolites and serum levels of LRP1 and Aβ1-42 proteins and oxidative stress enzymes and lipid and cognitive tests of Iranian AD patients. Methods In this double-blind placebo-controlled study, thirty-six mild AD patients were divided into groups (n=12) of silymarin 140mg, placebo, and rosuvastatin 10mg. Medications were administered 3 times a day for 6 months. Clinical tests, lipid profile (TG, HDL, TC, and LDL), Aβ1-42, and LRP1 markers were measured at the beginning and end of the intervention. Magnetic resonance spectroscopy (MRS) was used to measure metabolites. Using SPSS software a one-way ANOVA test was used to compare the means of the quantitative variables and Pearson and Spearman's correlations to measure the correlation. GraphPad Prism software was used for drawing graphs. P < 0.05 was considered a significant. Results The levels of LRP1 and Aβ1-42 in the silymarin group were significantly increased compared to the other groups (P < 0.05). NAA/mI in the silymarin group had a significant increase compared to both placebo and rosuvastatin groups (P < 0.05). Right and left hippocampal mI/Cr directly correlated with TG (r = 0.603, P = 0.003 and r = 0.595, P = 0.004, respectively). NAA/Cr of the right and left hippocampus was inversely related to TG (r = -0.511, P = 0.0033, and r = -0.532, P = 0.0021, respectively). NAA/Cr and NAA/mI of bilateral hippocampi directly correlated with HDL (P < 0.05). An inverse correlation was observed between the Aβ1-42 and mI/Cr of the right and left hippocampus (r = -0.661, P = 0.000 and r = -0.638, P = 0.000, respectively). Conclusion Donepezil and silymarin improved lipid profile associated with increased NAA/Cr, and decreased mI/Cr, in AD patients. Biomarker NAA/mI can be clinically significant in examining AD pathology. Measurement of the lipid factors and neurometabolites can be a suitable method for monitoring this disease.
Collapse
Affiliation(s)
- Auob Rustamzadeh
- Department of Anatomy, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Nader Sadigh
- Department of Emergency Medicine, School of Medicine, Trauma and Injury Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Ronak Shabani
- Department of Anatomy, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Reza Ahadi
- Department of Anatomy, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Zahra Vahabi
- Department of Geriatric Medicine, Ziaeian Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Arash Shabani
- Saadatabad Medical Imaging Center, Department of Advanced Imaging and Image Processing, Tehran, Iran
| | - Nafiseh Mohebi
- Department of Neurology, Rasool Akram Hospital, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Fatemeh Khamseh
- Department of Neurology, Faculty of Medicine, Islamic Azad University, Tehran, Iran
| | - Masume Behruzi
- Department of Anatomy, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Fatemeh Moradi
- Department of Anatomy, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| |
Collapse
|
14
|
Song Y, Hupfeld KE, Davies-Jenkins CW, Zöllner HJ, Murali-Manohar S, Mumuni AN, Crocetti D, Yedavalli V, Oeltzschner G, Alessi N, Batschelett MA, Puts NAJ, Mostofsky SH, Edden RAE. Brain Glutathione and GABA+ levels in autistic children. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.28.559718. [PMID: 37808813 PMCID: PMC10557661 DOI: 10.1101/2023.09.28.559718] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by social communication challenges and repetitive behaviors. Altered neurometabolite levels, including glutathione (GSH) and gamma-aminobutyric acid (GABA), have been proposed as potential contributors to the biology underlying ASD. This study investigated whether cerebral GSH or GABA levels differ between a large cohort of children aged 8-12 years with ASD (n=52) and typically developing children (TDC, n=49). A comprehensive analysis of GSH and GABA levels in multiple brain regions, including the primary motor cortex (SM1), thalamus (Thal), medial prefrontal cortex (mPFC), and supplementary motor area (SMA), was conducted using single-voxel HERMES MR spectroscopy at 3T. The results revealed no significant differences in cerebral GSH or GABA levels between the ASD and TDC groups across all examined regions. These findings suggest that the concentrations of GSH (an important antioxidant and neuromodulator) and GABA (a major inhibitory neurotransmitter) do not exhibit marked alterations in children with ASD compared to TDC. A statistically significant positive correlation was observed between GABA levels in the SM1 and Thal regions with ADHD inattention scores. No significant correlation was found between metabolite levels and hyper/impulsive scores of ADHD, measures of core ASD symptoms (ADOS-2, SRS-P) or adaptive behavior (ABAS-2). While both GSH and GABA have been implicated in various neurological disorders, the current study provides valuable insights into the specific context of ASD and highlights the need for further research to explore other neurochemical alterations that may contribute to the pathophysiology of this complex disorder.
Collapse
Affiliation(s)
- Yulu Song
- The Russel H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Kathleen E Hupfeld
- The Russel H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Christopher W Davies-Jenkins
- The Russel H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Helge J Zöllner
- The Russel H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Saipavitra Murali-Manohar
- The Russel H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| | | | - Deana Crocetti
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Vivek Yedavalli
- The Russel H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Georg Oeltzschner
- The Russel H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Natalie Alessi
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Mitchell A Batschelett
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Nicolaas A J Puts
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, United Kingdom
- MRC Center for Neurodevelopmental Disorders, King's College London, London, United Kingdom
| | - Stewart H Mostofsky
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, United States
- Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Department of Psychiatry and Behavioral Sciences, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Richard A E Edden
- The Russel H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| |
Collapse
|
15
|
Gudmundson AT, Koo A, Virovka A, Amirault AL, Soo M, Cho JH, Oeltzschner G, Edden RAE, Stark CEL. Meta-analysis and open-source database for in vivo brain Magnetic Resonance spectroscopy in health and disease. Anal Biochem 2023; 676:115227. [PMID: 37423487 PMCID: PMC10561665 DOI: 10.1016/j.ab.2023.115227] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Revised: 06/15/2023] [Accepted: 06/26/2023] [Indexed: 07/11/2023]
Abstract
Proton (1H) Magnetic Resonance Spectroscopy (MRS) is a non-invasive tool capable of quantifying brain metabolite concentrations in vivo. Prioritization of standardization and accessibility in the field has led to the development of universal pulse sequences, methodological consensus recommendations, and the development of open-source analysis software packages. One on-going challenge is methodological validation with ground-truth data. As ground-truths are rarely available for in vivo measurements, data simulations have become an important tool. The diverse literature of metabolite measurements has made it challenging to define ranges to be used within simulations. Especially for the development of deep learning and machine learning algorithms, simulations must be able to produce accurate spectra capturing all the nuances of in vivo data. Therefore, we sought to determine the physiological ranges and relaxation rates of brain metabolites which can be used both in data simulations and as reference estimates. Using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, we've identified relevant MRS research articles and created an open-source database containing methods, results, and other article information as a resource. Using this database, expectation values and ranges for metabolite concentrations and T2 relaxation times are established based upon a meta-analyses of healthy and diseased brains.
Collapse
Affiliation(s)
- Aaron T Gudmundson
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Annie Koo
- Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA, USA
| | - Anna Virovka
- Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA, USA
| | - Alyssa L Amirault
- Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA, USA
| | - Madelene Soo
- Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA, USA
| | - Jocelyn H Cho
- Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA, USA
| | - Georg Oeltzschner
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Richard A E Edden
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Craig E L Stark
- Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA, USA.
| |
Collapse
|
16
|
Zöllner HJ, Davies-Jenkins CW, Lee EG, Hendrickson TJ, Clarke WT, Edden RAE, Wisnowski JL, Gudmundson AT, Oeltzschner G. Continuous Automated Analysis Workflow for MRS Studies. J Med Syst 2023; 47:69. [PMID: 37418036 PMCID: PMC10947169 DOI: 10.1007/s10916-023-01969-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Accepted: 07/02/2023] [Indexed: 07/08/2023]
Abstract
Magnetic resonance spectroscopy (MRS) can non-invasively measure levels of endogenous metabolites in living tissue and is of great interest to neuroscience and clinical research. To this day, MRS data analysis workflows differ substantially between groups, frequently requiring many manual steps to be performed on individual datasets, e.g., data renaming/sorting, manual execution of analysis scripts, and manual assessment of success/failure. Manual analysis practices are a substantial barrier to wider uptake of MRS. They also increase the likelihood of human error and prevent deployment of MRS at large scale. Here, we demonstrate an end-to-end workflow for fully automated data uptake, processing, and quality review.The proposed continuous automated MRS analysis workflow integrates several recent innovations in MRS data and file storage conventions. They are efficiently deployed by a directory monitoring service that automatically triggers the following steps upon arrival of a new raw MRS dataset in a project folder: (1) conversion from proprietary manufacturer file formats into the universal format NIfTI-MRS; (2) consistent file system organization according to the data accumulation logic standard BIDS-MRS; (3) executing a command-line executable of our open-source end-to-end analysis software Osprey; (4) e-mail delivery of a quality control summary report for all analysis steps.The automated architecture successfully completed for a demonstration dataset. The only manual step required was to copy a raw data folder into a monitored directory.Continuous automated analysis of MRS data can reduce the burden of manual data analysis and quality control, particularly for non-expert users and multi-center or large-scale studies and offers considerable economic advantages.
Collapse
Affiliation(s)
- Helge Jörn Zöllner
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, 600 N Wolfe St, Baltimore, MD, 21287, USA.
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA.
| | - Christopher W Davies-Jenkins
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, 600 N Wolfe St, Baltimore, MD, 21287, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Erik G Lee
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Informatics Institute, University of Minnesota, Minneapolis, MN, USA
| | - Timothy J Hendrickson
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Informatics Institute, University of Minnesota, Minneapolis, MN, USA
| | - William T Clarke
- Wellcome Centre for Integrative Neuroimaging, Department of Clinical Neurosciences, FMRIB, University of Oxford, Oxford, Nuffield, UK
| | - Richard A E Edden
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, 600 N Wolfe St, Baltimore, MD, 21287, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Jessica L Wisnowski
- Department of Radiology, Keck School of Medicine, Children's Hospital Los Angeles, University of Southern California, Los Angeles, USA
- Fetal and Neonatal Institute, CHLA Division of Neonatology, Department of Pediatrics, Keck School of Medicine, Children's Hospital Los Angeles, University of Southern California, Los Angeles, USA
| | - Aaron T Gudmundson
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, 600 N Wolfe St, Baltimore, MD, 21287, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Georg Oeltzschner
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, 600 N Wolfe St, Baltimore, MD, 21287, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| |
Collapse
|
17
|
Gudmundson AT, Koo A, Virovka A, Amirault AL, Soo M, Cho JH, Oeltzschner G, Edden RA, Stark C. Meta-analysis and Open-source Database for In Vivo Brain Magnetic Resonance Spectroscopy in Health and Disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.10.528046. [PMID: 37205343 PMCID: PMC10187197 DOI: 10.1101/2023.02.10.528046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
Proton ( 1 H) Magnetic Resonance Spectroscopy (MRS) is a non-invasive tool capable of quantifying brain metabolite concentrations in vivo . Prioritization of standardization and accessibility in the field has led to the development of universal pulse sequences, methodological consensus recommendations, and the development of open-source analysis software packages. One on-going challenge is methodological validation with ground-truth data. As ground-truths are rarely available for in vivo measurements, data simulations have become an important tool. The diverse literature of metabolite measurements has made it challenging to define ranges to be used within simulations. Especially for the development of deep learning and machine learning algorithms, simulations must be able to produce accurate spectra capturing all the nuances of in vivo data. Therefore, we sought to determine the physiological ranges and relaxation rates of brain metabolites which can be used both in data simulations and as reference estimates. Using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, we've identified relevant MRS research articles and created an open-source database containing methods, results, and other article information as a resource. Using this database, expectation values and ranges for metabolite concentrations and T 2 relaxation times are established based upon a meta-analyses of healthy and diseased brains.
Collapse
Affiliation(s)
- Aaron T. Gudmundson
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD
| | - Annie Koo
- Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA
| | - Anna Virovka
- Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA
| | - Alyssa L. Amirault
- Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA
| | - Madelene Soo
- Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA
| | - Jocelyn H. Cho
- Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA
| | - Georg Oeltzschner
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD
| | - Richard A.E. Edden
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD
| | - Craig Stark
- Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA
| |
Collapse
|
18
|
Soher BJ, Semanchuk P, Todd D, Ji X, Deelchand D, Joers J, Oz G, Young K. Vespa: Integrated applications for RF pulse design, spectral simulation and MRS data analysis. Magn Reson Med 2023. [PMID: 37183778 DOI: 10.1002/mrm.29686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 03/24/2023] [Accepted: 04/12/2023] [Indexed: 05/16/2023]
Abstract
PURPOSE The Vespa package (Versatile Simulation, Pulses, and Analysis) is described and demonstrated. It provides workflows for developing and optimizing linear combination modeling (LCM) fitting for 1 H MRS data using intuitive graphical user interface interfaces for RF pulse design, spectral simulation, and MRS data analysis. Command line interfaces for embedding workflows in MR manufacturer platforms and utilities for synthetic dataset creation are included. Complete provenance is maintained for all steps in workflows. THEORY AND METHODS Vespa is written in Python for compatibility across operating systems. It embeds the PyGAMMA spectral simulation library for spectral simulation. Multiprocessing methods accelerate processing and visualization. Applications use the Vespa database for results storage and cross-application access. Three projects demonstrate pulse, sequence, simulation, and data analysis workflows: (1) short TE semi-LASER single-voxel spectroscopy (SVS) LCM fitting, (2) optimizing MEGA-PRESS (MEscher-GArwood Point RESolved Spectroscopy) flip angle and LCM fitting, and (3) creating a synthetic short TE dataset. RESULTS The LCM workflows for in vivo basis set creation and spectral analysis showed reasonable results for both the short TE semi-LASER and MEGA-PRESS. Examples of pulses, simulations, and data fitting are shown in Vespa application interfaces for various steps to demonstrate the interactive workflow. CONCLUSION Vespa provides an efficient and extensible platform for characterizing RF pulses, pulse design, spectral simulation optimization, and automated LCM fitting via an interactive platform. Modular design and command line interface make it easy to embed in other platforms. As open source, it is free to the MRS community for use and extension. Vespa source code and documentation are available through GitHub.
Collapse
Affiliation(s)
- Brian J Soher
- Center for Advanced MR Development, Department of Radiology, Duke University Medical Center, Durham, NC, USA
| | - Philip Semanchuk
- Center for Advanced MR Development, Department of Radiology, Duke University Medical Center, Durham, NC, USA
| | - David Todd
- Center for Imaging of Neurodegenerative Disorders, University of California, San Francisco, CA, USA
| | - Xiao Ji
- Center for Advanced MR Development, Department of Radiology, Duke University Medical Center, Durham, NC, USA
| | - Dinesh Deelchand
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota Medical School, Minneapolis, MN, USA
| | - James Joers
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota Medical School, Minneapolis, MN, USA
| | - Gulin Oz
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota Medical School, Minneapolis, MN, USA
| | - Karl Young
- Department of Radiology, University of California, San Francisco, CA, USA
| |
Collapse
|
19
|
Zöllner HJ, Thiel TA, Füllenbach ND, Jördens MS, Ahn S, Wilms LM, Ljimani A, Häussinger D, Butz M, Wittsack HJ, Schnitzler A, Oeltzschner G. J-difference GABA-edited MRS reveals altered cerebello-thalamo-cortical metabolism in patients with hepatic encephalopathy. Metab Brain Dis 2023; 38:1221-1238. [PMID: 36729261 PMCID: PMC10897767 DOI: 10.1007/s11011-023-01174-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 01/17/2023] [Indexed: 02/03/2023]
Abstract
Hepatic encephalopathy (HE) is a common neurological manifestation of liver cirrhosis and is characterized by an increase of ammonia in the brain accompanied by a disrupted neurotransmitter balance, including the GABAergic and glutamatergic systems. The aim of this study is to investigate metabolic abnormalities in the cerebello-thalamo-cortical system of HE patients using GABA-edited MRS and links between metabolite levels, disease severity, critical flicker frequency (CFF), motor performance scores, and blood ammonia levels. GABA-edited MRS was performed in 35 participants (16 controls, 19 HE patients) on a clinical 3 T MRI system. MRS voxels were placed in the right cerebellum, left thalamus, and left motor cortex. Levels of GABA+ and of other metabolites of interest (glutamine, glutamate, myo-inositol, glutathione, total choline, total NAA, and total creatine) were assessed. Group differences in metabolite levels and associations with clinical metrics were tested. GABA+ levels were significantly increased in the cerebellum of patients with HE. GABA+ levels in the motor cortex were significantly decreased in HE patients, and correlated with the CFF (r = 0.73; p < .05) and motor performance scores (r = -0.65; p < .05). Well-established HE-typical metabolite patterns (increased glutamine, decreased myo-inositol and total choline) were confirmed in all three regions and were closely linked to clinical metrics. In summary, our findings provide further evidence for alterations in the GABAergic system in the cerebellum and motor cortex in HE. These changes were accompanied by characteristic patterns of osmolytes and oxidative stress markers in the cerebello-thalamo-cortical system. These metabolic disturbances are a likely contributor to HE motor symptoms in HE. In patients with hepatic encephalopathy, GABA+ levels in the cerebello-thalamo-cortical loop are significantly increased in the cerebellum and significantly decreased in the motor cortex. GABA+ levels in the motor cortex strongly correlate with critical flicker frequency (CFF) and motor performance score (pegboard test tPEG), but not blood ammonia levels (NH3).
Collapse
Affiliation(s)
- Helge Jörn Zöllner
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA.
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany.
- Department of Diagnostic and Interventional Radiology, Medical Faculty, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany.
| | - Thomas A Thiel
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
- Department of Diagnostic and Interventional Radiology, Medical Faculty, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
| | - Nur-Deniz Füllenbach
- Department of Gastroenterology, Hepatology and Infectiology, Medical Faculty, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
| | - Markus S Jördens
- Department of Gastroenterology, Hepatology and Infectiology, Medical Faculty, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
| | | | - Lena M Wilms
- Department of Diagnostic and Interventional Radiology, Medical Faculty, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
| | - Alexandra Ljimani
- Department of Diagnostic and Interventional Radiology, Medical Faculty, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
| | - Dieter Häussinger
- Department of Gastroenterology, Hepatology and Infectiology, Medical Faculty, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
| | - Markus Butz
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
| | - Hans-Jörg Wittsack
- Department of Diagnostic and Interventional Radiology, Medical Faculty, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
| | - Alfons Schnitzler
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
| | - Georg Oeltzschner
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| |
Collapse
|
20
|
Hupfeld KE, Zöllner HJ, Oeltzschner G, Hyatt HW, Herrmann O, Gallegos J, Hui SCN, Harris AD, Edden RAE, Tsapkini K. Brain total creatine differs between primary progressive aphasia (PPA) subtypes and correlates with disease severity. Neurobiol Aging 2023; 122:65-75. [PMID: 36508896 PMCID: PMC9839619 DOI: 10.1016/j.neurobiolaging.2022.11.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 11/01/2022] [Accepted: 11/04/2022] [Indexed: 11/18/2022]
Abstract
Primary progressive aphasia (PPA) is comprised of three subtypes: logopenic (lvPPA), non-fluent (nfvPPA), and semantic (svPPA). We used magnetic resonance spectroscopy (MRS) to measure tissue-corrected metabolite levels in the left inferior frontal gyrus (IFG) and right sensorimotor cortex (SMC) from 61 PPA patients. We aimed to: (1) characterize subtype differences in metabolites; and (2) test for metabolite associations with symptom severity. tCr differed by subtype across the left IFG and right SMC. tCr levels were lowest in lvPPA and highest in svPPA. tCr levels predicted lvPPA versus svPPA diagnosis. Higher IFG tCr and lower Glx correlated with greater disease severity. As tCr is involved in brain energy metabolism, svPPA pathology might involve changes in specific cellular energy processes. Perturbations to cellular energy homeostasis in language areas may contribute to symptoms. Reduced cortical excitatory capacity (i.e. lower Glx) in language regions may also contribute to symptoms. Thus, tCr may be useful for differentiating between PPA subtypes, and both tCr and Glx might have utility in understanding PPA mechanisms and tracking progression.
Collapse
Affiliation(s)
- Kathleen E Hupfeld
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Helge J Zöllner
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Georg Oeltzschner
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Hayden W Hyatt
- Department of Physiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Olivia Herrmann
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Jessica Gallegos
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Steve C N Hui
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Ashley D Harris
- Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada; Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada
| | - Richard A E Edden
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Kyrana Tsapkini
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Cognitive Science, Johns Hopkins University, Baltimore, MD, USA.
| |
Collapse
|
21
|
Hupfeld KE, Zöllner HJ, Hui SCN, Song Y, Murali-Manohar S, Yedavalli V, Oeltzschner G, Prisciandaro JJ, Edden RAE. Impact of acquisition and modeling parameters on test-retest reproducibility of edited GABA. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.20.524952. [PMID: 36712103 PMCID: PMC9882325 DOI: 10.1101/2023.01.20.524952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Literature values for within-subject test-retest reproducibility of gamma-aminobutyric acid (GABA), measured with edited magnetic resonance spectroscopy (MRS), vary widely. Reasons for this variation remain unclear. Here we tested whether sequence complexity (two-experiment MEGA-PRESS versus four-experiment HERMES), editing pulse duration (14 versus 20 ms), scanner frequency drift (interleaved water referencing (IWR) turned ON versus OFF), and linear combination modeling variations (three different co-edited macromolecule models and 0.55 versus 0.4 ppm spline baseline knot spacing) affected the within-subject coefficient of variation of GABA + macromolecules (GABA+). We collected edited MRS data from the dorsal anterior cingulate cortex from 20 participants (30.8 ± 9.5 years; 10 males). Test and retest scans were separated by removing the participant from the scanner for 5-10 minutes. Each acquisition consisted of two MEGA-PRESS and two HERMES sequences with editing pulse durations of 14 and 20 ms (referred to here as: MEGA-14, MEGA-20, HERMES-14, and HERMES-20; all TE = 80 ms, 224 averages). Reproducibility did not consistently differ for MEGA-PRESS compared with HERMES or for 14 compared with 20 ms editing pulses. A composite model of the 0.9 and 3 ppm macromolecules (particularly for HERMES) and sparser (0.55 compared with 0.4 ppm) spline baseline knot spacing yielded generally better test-retest reproducibility for GABA+. Replicating our prior results, linear combination modeling in Osprey compared with simple peak fitting in Gannet resulted in substantially better test-retest reproducibility. These results highlight the importance of model selection for edited MRS studies of GABA+, particularly for clinical studies which focus on individual patient differences in GABA+ or changes following an intervention.
Collapse
|
22
|
Hui SC, Gong T, Zöllner HJ, Hupfeld KE, Gudmundson AT, Murali-Manohar S, Davies-Jenkins CW, Song Y, Chen Y, Oeltzschner G, Wang G, Edden RAE. sLASER and PRESS Perform Similarly at Revealing Metabolite-Age Correlations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.18.524597. [PMID: 36711794 PMCID: PMC9882274 DOI: 10.1101/2023.01.18.524597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Purpose To compare the respective ability of PRESS and sLASER to reveal biological relationships, using age as a validation covariate. Methods MRS data were acquired from 102 healthy volunteers using PRESS and sLASER in centrum semiovale (CSO) and posterior cingulate cortex (PCC) regions. Acquisition parameters included TR/TE 2000/30 ms; 96 transients; 2048 datapoints sampled at 2 kHz.Spectra were analyzed using Osprey. Signal-to-noise ratio (SNR), full-width-half-maximum linewidth of tCr, and metabolite concentrations were extracted. A linear model was used to compare SNR and linewidth. Paired t-tests were used to assess differences in metabolite measurements between PRESS and sLASER. Correlations were used to evaluate the relationship between PRESS and sLASER metabolite estimates, as well as the strength of each metabolite-age relationship. Coefficients of variation were calculated to assess inter-subject variability in each metabolite measurement. Results SNR and linewidth were significantly higher (p<0.05) for sLASER than PRESS. Paired t-tests showed significant differences between PRESS and sLASER in most metabolite measurements. Metabolite measures were significantly correlated (p<0.05) for most metabolites between the two methods except GABA, Gln and Lac in CSO and GSH, Lac and NAAG in PCC. Metabolite-age relationships were consistently identified using both PRESS and sLASER. Similar CVs were observed for most metabolites. Conclusion The study results suggest strong agreement between PRESS and sLASER in identifying relationships between brain metabolites and age in CSO and PCC data acquired at 3T. sLASER is technically desirable due to the reduced chemical shift displacement artifact; however, PRESS performed similarly in 'good' brain regions at clinical field strength.
Collapse
Affiliation(s)
- Steve C.N. Hui
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Tao Gong
- Department of Radiology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Helge J. Zöllner
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Kathleen E. Hupfeld
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Aaron T. Gudmundson
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Saipavitra Murali-Manohar
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Christopher W. Davies-Jenkins
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Yulu Song
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Yufan Chen
- Department of Radiology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Georg Oeltzschner
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Guangbin Wang
- Department of Radiology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Richard A. E. Edden
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| |
Collapse
|